When I started out using neural networks to buy half hour blocs of television time I used C++ to write the algorythms I needed. When I decided to write the code here I decded to use ML.NET with Electron.NET and here is why...

Our Neural Networks use ML.NET because it offers a major advantage for developers aiming to integrate neural networks into commercial applications, particularly when using a C# .NET 8 Core Razor Web App in Electron.NET. This setup ensures seamless cross-platform compatibility across both Mac and Windows environments, eliminating the need for additional files and third-party dependencies associated with extremely problematic Python-based solutions.

One of the standout features of ML.NET is its deep integration with the .NET ecosystem. Developers can leverage their existing C# skills to build, train, and deploy machine learning models without transitioning to a different programming language or platform. This integration streamlines the development process, reduces potential errors, and enhances maintainability. In contrast, incorporating Python into a .NET application requires managing separate runtimes and dependencies, which seriously complicate deployment and increase the application's footprint.

Performance benchmarks have demonstrated that ML.NET can train models with high efficiency. For instance, using a 9GB Amazon review dataset, ML.NET achieved a 95% accuracy rate in sentiment analysis. Other popular machine learning frameworks encountered memory errors with this dataset, highlighting ML.NET's robustness and scalability.

While Python boasts a modest ecosystem of machine learning libraries, it presents too many challenges to use in a commercial software context. Python's interpreted nature leads to slower execution times compared to compiled languages like C#. Additionally, managing Python's package dependencies is very cumbersome, often resulting in version conflicts and serious deployment issues. These factors seriously hinder the development and maintenance of commercial applications.

In summary, ML.NET provides a superior solution for integrating neural networks into commercial applications. Its seamless integration with the .NET ecosystem, cross-platform capabilities, and efficient performance make it an ideal choice for developers seeking to build robust, scalable, and maintainable machine learning solutions without the complexities associated with Python.

Many years ago I used Bayesian Optimization and neural networks I created to find the most profitable stations for buying ads by optimizing the allocation of a fixed budget across the different stations. The objective function that we want to optimize is the net profit, which is the difference between the revenue generated from the ads and the cost of placing the ads on each station.

To use Bayesian Optimization, I first defined a surrogate function that approximates the objective function. The surrogate function is typically a probabilistic model, such as a Gaussian process or a tree-based model, that takes the allocation of the budget as input and predicts the expected net profit.

I then used an acquisition function to determine the next allocation of the budget to evaluate. The acquisition function balances exploration and exploitation by considering both the expected improvement of the surrogate function and the uncertainty of the model predictions.

Once we have evaluated the surrogate function at the chosen allocation, we update the model parameters and continue the optimization process until we reach a stopping criterion.

To determine how to allocate a fixed amount of money across the stations to get the maximum profit, we can use Bayesian Optimization to find the allocation that maximizes the expected net profit. We can start by defining a search space that consists of the possible allocations of the budget across the different stations. For example, if we have three stations, we can define the search space as the set of all possible triplets of budget allocations.

We can then use Bayesian Optimization to search this space for the allocation that maximizes the expected net profit. The output of the optimization process will be the optimal allocation of the budget across the stations, along with the expected net profit that can be achieved with this allocation.

In summary, Bayesian Optimization can be used to find the most profitable stations for buying ads and determine how to allocate a fixed amount of money across these stations to get the maximum profit. The key steps are to define a surrogate function that approximates the objective function, use an acquisition function to guide the search process, and update the model parameters based on the observed data.

The ratio of GROSS SALES to MEDIA COST is called the "Pull Ratio" and it is the most importantor indicatior in buying any form of advertising. I was first introduced to the "Pull Ratio" by my first business partner who was a billionaire who controlled the remnant space in over 13,000 newspapers and magazines. In fact, he and I literally controlled the print mail order business in America for many years. His cost on a full paage ad in any newspaper or magazine was $4. That was $4 for full pages that sold for $100,000 and up. And he and I also ran most of the one minute, direct response television spots on a Per Inquiry basis with thousands of television stations.

In college I majored in chemistry and theoretical physics and after I finished medical school he and I teamed up because I was the clever guy who figured out how to legally get around issues with the Federal Trade Commission. I had the scientific and medical background to develop all kinds of products to sell in newspapers and magazines and on National Television and we had had a guaranteed NET Product on all media we ran--a cost of $4 on full-page ads in newspapers and magazines and we paid television stations 35% of sales so we were guaranteed a net profit on all our television ads.

When you start a new business you typically need to advertise and many people will use an ad agency. But the reality of how agencies buy advertsing for a client should scare YOU! Ad agencies get 10% to 15% of the media buy so they will always buy advertising at high prices to maximum their profits. If you don't realize this reality then don't waste your time reading further becasue you simply lack the basic business acumen needed to be a real mogul.

To really buy advertsing at the lowest prices simply create a separate corporation that will function as an advertsing agency to buy your advertsing and NEVER let the TV stations, print or Internet media know that your agency is connected to your business that you are buying the advertsing for!

When I was buying half-hour blocks of broacast television time I setup 22 separate ad agencies around the country to control all best half hours for my infomercials.

The BIG SECRET is that in almost every case no matter what form of advertising you do, the ratio of GROSS SALES to COST OF MEDIA on each media buy is a fixed constant! This fact makes it possible for a neural network to accurately buy advetising that guarantees a NET Profit.

If we know that the ratio of profit to cost of media on each station is a fixed constant, we can simplify the problem of finding the most profitable stations, or any place to advertise for buying ads and determining how to allocate a fixed amount of money across these stations to get the maximum profit.

Specifically, if the ratio of profit to cost is a fixed constant, then the net profit for each station will be proportional to the cost of the ad. This means that we can allocate the budget to each station in proportion to the cost of the ad, and this allocation will maximize the expected net profit.

To use Bayesian Optimization to determine the allocation that maximizes the expected net profit, we can define a search space that consists of the possible allocations of the budget across the different stations. For example, if we have three stations with costs:

c1, c2, and c3, we can define the search space as the set of 
        all possible triplets of budget allocations (x1, x2, x3) such 
        that x1 + x2 + x3 = B, where B is the total budget.

We can then use Bayesian Optimization to search this space for the allocation that maximizes the expected net profit. The surrogate function that we want to optimize is the expected net profit, which can be expressed as a function of the budget allocations:

(x1, x2, x3)

Once we have evaluated the surrogate function at the chosen allocation, we update the model parameters and continue the optimization process until we reach a stopping criterion.

In summary, if the ratio of gross sales (or profit) to cost of media on each station is a fixed constant, we can use Bayesian Optimization to determine how to allocate a fixed amount of money across these stations to get the maximum net profit. We can allocate the budget to each station in proportion to the cost of the ad, and use Bayesian Optimization to search for the optimal allocation that maximizes the expected net profit. The key steps are to define a search space that consists of the possible allocations of the budget, use a surrogate function to approximate the expected net profit, use an acquisition function to guide the search process, and update the model parameters based on the observed data.

This approach allowed myself, Bill SerGio, to create over 100 infomercials starring famous celebrities that made me a very LARGE fortune. Amoung those over 100 profitable infomercials were 22 infomercials that each grossed from $100 million to $500 million in their first year where net profit on each was over 80% of the gross using the neural networks I created. I want to share what I did in creating those neural networks and show that they apply to any source of advertising and not just to buying television time.